Refine
Year of publication
Document Type
- Lecture (27)
- Conference Proceeding (16)
- Article (5)
Keywords
- eHealth (13)
- Rehabilitation (10)
- Biomedical Engineering (8)
- Breathing Simulation (6)
- Lung Simulator (6)
- Education (3)
- Interoperability (3)
- Neurology (3)
- Stroke Patients (3)
- Telemonitoring (3)
- App development (2)
- Breathing (2)
- Electronic Health Records (2)
- Exercise database (2)
- Flow Measurement (2)
- Home-based Rehabilitation (2)
- In-silico Models (2)
- Low-Cost (2)
- Mathematical Models (2)
- Mechanical Simulation (2)
- Rehabitation (2)
- Telerehabilitation (2)
- mechanical lung-simulator (2)
- Biofeedback System (1)
- Biomedical Engineering, Breathing simulation (1)
- Dry powder inhaler resistance (1)
- Electromechanical lung simulator (1)
- Emergency Room (1)
- Harmonization (1)
- Health Applications (1)
- Healthcare (1)
- Home based rehabilitation (1)
- Hospital (1)
- Individualized Rehabilitation (1)
- Insoles (1)
- Internationalized Teaching (1)
- Life Science Engineering (1)
- Lung Simulation (1)
- Medical Training (1)
- Mobile Application (1)
- Neurological Rehabilitation (1)
- Pilot Study (1)
- Research Project (1)
- Shoes (1)
- Simulator Sickness (1)
- Teaching (1)
- Telemedicine (1)
- User Centered Design (1)
- Virtual Environment (1)
- Virtual Reality (1)
- Virtual Supermarket (1)
- Weight bearing (1)
- Weight-Bearing (1)
- e-Health (1)
- inspiratory flow rate (1)
- lung simulation (1)
- mechanical upper airway model (1)
- optical aerosol spectrometry (1)
- telemedicine (1)
Department
- Department Life Science Engineering (48) (remove)
During mechanical ventilation, a disparity between flow, pressure and volume demands of the patient and the assistance delivered by the mechanical ventilator often occurs. This paper introduces an alternative approach of simulating and evaluating patient–ventilator interactions with high fidelity using the electromechanical lung simulator xPULM™. The xPULM™ approximates respiratory activities of a patient during alternating phases of spontaneous breathing and apnea intervals while connected to a mechanical ventilator. Focusing on different triggering events, volume assist-control (V/A-C) and pressure support ventilation (PSV) modes were chosen to test patient–ventilator interactions. In V/A-C mode, a double-triggering was detected every third breathing cycle, leading to an asynchrony index of 16.67%, which is classified as severe. This asynchrony causes a significant increase of peak inspiratory pressure (7.96 ± 6.38 vs. 11.09 ± 0.49 cmH2O, p < 0.01)) and peak expiratory flow (−25.57 ± 8.93 vs. 32.90 ± 0.54 L/min, p < 0.01) when compared to synchronous phases of the breathing simulation. Additionally, events of premature cycling were observed during PSV mode. In this mode, the peak delivered volume during simulated spontaneous breathing phases increased significantly (917.09 ± 45.74 vs. 468.40 ± 31.79 mL, p < 0.01) compared to apnea phases. Various dynamic clinical situations can be approximated using this approach and thereby could help to identify undesired patient–ventilation interactions in the future. Rapidly manufactured ventilator systems could also be tested using this approach. View Full-Text
LUMOR: An App for Standardized Control and Monitoring of a Porcine Lung and its Nutrient Cycle
(2014)